http://pandas.pydata.org/pandas-docs/dev/merging.html does not have a hook function to do this on the fly. Would be nice though…

I would just do a separate step and use difflib getclosest_matches to create a new column in one of the 2 dataframes and the merge/join on the fuzzy matched column

As a heads up, this basically works, except if no match is found, or if you have NaNs in either column. Instead of directly applying get_close_matches, I found it easier to apply the following function. The choice of NaN replacements will depend a lot on your dataset.

def fuzzy_match(a, b):
    left = '1' if pd.isnull(a) else a
    right = b.fillna('2')
    out = difflib.get_close_matches(left, right)
    return out[0] if out else np.NaN

I would use Jaro-Winkler, because it is one of the most performant and accurate approximate string matching algorithms currently available [Cohen, et al.], [Winkler].

This is how I would do it with Jaro-Winkler from the jellyfish package:

def get_closest_match(x, list_strings):

  best_match = None
  highest_jw = 0

  for current_string in list_strings:
    current_score = jellyfish.jaro_winkler(x, current_string)

    if(current_score > highest_jw):
      highest_jw = current_score
      best_match = current_string

  return best_match

df1 = pandas.DataFrame([[1],[2],[3],[4],[5]], index=['one','two','three','four','five'], columns=['number'])
df2 = pandas.DataFrame([['a'],['b'],['c'],['d'],['e']], index=['one','too','three','fours','five'], columns=['letter'])

df2.index = df2.index.map(lambda x: get_closest_match(x, df1.index))

df1.join(df2)

Output:

    number  letter
one     1   a
two     2   b
three   3   c
four    4   d
five    5   e

I have written a Python package which aims to solve this problem:

pip install fuzzymatcher

You can find the repo here and docs here.

Basic usage:

Given two dataframes df_left and df_right, which you want to fuzzy join, you can write the following:

from fuzzymatcher import link_table, fuzzy_left_join

# Columns to match on from df_left
left_on = ["fname", "mname", "lname",  "dob"]

# Columns to match on from df_right
right_on = ["name", "middlename", "surname", "date"]

# The link table potentially contains several matches for each record
fuzzymatcher.link_table(df_left, df_right, left_on, right_on)

Or if you just want to link on the closest match:

fuzzymatcher.fuzzy_left_join(df_left, df_right, left_on, right_on)

You can use d6tjoin for that

import d6tjoin.top1
d6tjoin.top1.MergeTop1(df1.reset_index(),df2.reset_index(),
       fuzzy_left_on=['index'],fuzzy_right_on=['index']).merge()['merged']


index number index_right letter
0 one 1 one a
1 two 2 too b
2 three 3 three c
3 four 4 fours d
4 five 5 five e


It has a variety of additional features such as:

• check join quality, pre and post join
• customize similarity function, eg edit distance vs hamming distance
• specify max distance
• multi-core compute

For details see

• MergeTop1 examples – Best match join examples notebook
• PreJoin examples – Examples for diagnosing join problems

Using fuzzywuzzy

Since there are no examples with the fuzzywuzzy package, here’s a function I wrote which will return all matches based on a threshold you can set as a user:

Example datframe

df1 = pd.DataFrame({'Key':['Apple', 'Banana', 'Orange', 'Strawberry']})
df2 = pd.DataFrame({'Key':['Aple', 'Mango', 'Orag', 'Straw', 'Bannanna', 'Berry']})

# df1
          Key
0       Apple
1      Banana
2      Orange
3  Strawberry

# df2
        Key
0      Aple
1     Mango
2      Orag
3     Straw
4  Bannanna
5     Berry

Function for fuzzy matching

def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
    """
    :param df_1: the left table to join
    :param df_2: the right table to join
    :param key1: key column of the left table
    :param key2: key column of the right table
    :param threshold: how close the matches should be to return a match, based on Levenshtein distance
    :param limit: the amount of matches that will get returned, these are sorted high to low
    :return: dataframe with boths keys and matches
    """
    s = df_2[key2].tolist()
    
    m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))    
    df_1['matches'] = m
    
    m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold]))
    df_1['matches'] = m2
    
    return df_1

Using our function on the dataframes: #1

from fuzzywuzzy import fuzz
from fuzzywuzzy import process

fuzzy_merge(df1, df2, 'Key', 'Key', threshold=80)

          Key       matches
0       Apple          Aple
1      Banana      Bannanna
2      Orange          Orag
3  Strawberry  Straw, Berry

Using our function on the dataframes: #2

df1 = pd.DataFrame({'Col1':['Microsoft', 'Google', 'Amazon', 'IBM']})
df2 = pd.DataFrame({'Col2':['Mcrsoft', 'gogle', 'Amason', 'BIM']})

fuzzy_merge(df1, df2, 'Col1', 'Col2', 80)

        Col1  matches
0  Microsoft  Mcrsoft
1     Google    gogle
2     Amazon   Amason
3        IBM         

Installation:

Pip

pip install fuzzywuzzy

Anaconda

conda install -c conda-forge fuzzywuzzy

I used Fuzzymatcher package and this worked well for me. Visit this link for more details on this.

use the below command to install

pip install fuzzymatcher

Below is the sample Code (already submitted by RobinL above)

from fuzzymatcher import link_table, fuzzy_left_join

# Columns to match on from df_left
left_on = ["fname", "mname", "lname",  "dob"]

# Columns to match on from df_right
right_on = ["name", "middlename", "surname", "date"]

# The link table potentially contains several matches for each record
fuzzymatcher.link_table(df_left, df_right, left_on, right_on)

Errors you may get

1. ZeroDivisionError: float division by zero—> Refer to this
   link to resolve it
2. OperationalError: No Such Module:fts4 –> downlaod the sqlite3.dll
   from here and replace the DLL file in your python or anaconda
   DLLs folder.

Pros :

1. Works faster. In my case, I compared one dataframe with 3000 rows with anohter dataframe with 170,000 records . This also uses SQLite3 search across text. So faster than many
2. Can check across multiple columns and 2 dataframes. In my case, I was looking for closest match based on address and company name. Sometimes, company name might be same but address is the good thing to check too.
3. Gives you score for all the closest matches for the same record. you choose whats the cutoff score.

cons:

1. Original package installation is buggy
2. Required C++ and visual studios installed too
3. Wont work for 64 bit anaconda/Python

There is a package called fuzzy_pandas that can use levenshtein, jaro, metaphone and bilenco methods. With some great examples here

import pandas as pd
import fuzzy_pandas as fpd

df1 = pd.DataFrame({'Key':['Apple', 'Banana', 'Orange', 'Strawberry']})
df2 = pd.DataFrame({'Key':['Aple', 'Mango', 'Orag', 'Straw', 'Bannanna', 'Berry']})

results = fpd.fuzzy_merge(df1, df2,
            left_on='Key',
            right_on='Key',
            method='levenshtein',
            threshold=0.6)

results.head()

  Key    Key
0 Apple  Aple
1 Banana Bannanna
2 Orange Orag

For a general approach: fuzzy_merge

For a more general scenario in which we want to merge columns from two dataframes which contain slightly different strings, the following function uses difflib.get_close_matches along with merge in order to mimic the functionality of pandas’ merge but with fuzzy matching:

import difflib 

def fuzzy_merge(df1, df2, left_on, right_on, how='inner', cutoff=0.6):
    df_other= df2.copy()
    df_other[left_on] = [get_closest_match(x, df1[left_on], cutoff) 
                         for x in df_other[right_on]]
    return df1.merge(df_other, on=left_on, how=how)

def get_closest_match(x, other, cutoff):
    matches = difflib.get_close_matches(x, other, cutoff=cutoff)
    return matches[0] if matches else None

Here are some use cases with two sample dataframes:

print(df1)

     key   number
0    one       1
1    two       2
2  three       3
3   four       4
4   five       5

print(df2)

                 key_close  letter
0                    three      c
1                      one      a
2                      too      b
3                    fours      d
4  a very different string      e

With the above example, we’d get:

fuzzy_merge(df1, df2, left_on='key', right_on='key_close')

     key  number key_close letter
0    one       1       one      a
1    two       2       too      b
2  three       3     three      c
3   four       4     fours      d

And we could do a left join with:

fuzzy_merge(df1, df2, left_on='key', right_on='key_close', how='left')

     key  number key_close letter
0    one       1       one      a
1    two       2       too      b
2  three       3     three      c
3   four       4     fours      d
4   five       5       NaN    NaN

For a right join, we’d have all non-matching keys in the left dataframe to None:

fuzzy_merge(df1, df2, left_on='key', right_on='key_close', how='right')

     key  number                key_close letter
0    one     1.0                      one      a
1    two     2.0                      too      b
2  three     3.0                    three      c
3   four     4.0                    fours      d
4   None     NaN  a very different string      e

Also note that difflib.get_close_matches will return an empty list if no item is matched within the cutoff. In the shared example, if we change the last index in df2 to say:

print(df2)

                          letter
one                          a
too                          b
three                        c
fours                        d
a very different string      e

We’d get an index out of range error:

df2.index.map(lambda x: difflib.get_close_matches(x, df1.index)[0])

IndexError: list index out of range

In order to solve this the above function get_closest_match will return the closest match by indexing the list returned by difflib.get_close_matches only if it actually contains any matches.

For more complex use cases to match rows with many columns you can use recordlinkage package. recordlinkage provides all the tools to fuzzy match rows between pandas data frames which helps to deduplicate your data when merging. I have written a detailed article about the package here

if the join axis is numeric this could also be used to match indexes with a specified tolerance:

def fuzzy_left_join(df1, df2, tol=None):
    index1 = df1.index.values
    index2 = df2.index.values

    diff = np.abs(index1.reshape((-1, 1)) - index2)
    mask_j = np.argmin(diff, axis=1)  # min. of each column
    mask_i = np.arange(mask_j.shape[0])

    df1_ = df1.iloc[mask_i]
    df2_ = df2.iloc[mask_j]

    if tol is not None:
        mask = np.abs(df2_.index.values - df1_.index.values) <= tol
        df1_ = df1_.loc[mask]
        df2_ = df2_.loc[mask]

    df2_.index = df1_.index

    out = pd.concat([df1_, df2_], axis=1)
    return out

I have used fuzzywuzz in a very minimal way whilst matching the existing behaviour and keywords of merge in pandas.

Just specify your accepted threshold for matching (between 0 and 100):

from fuzzywuzzy import process

def fuzzy_merge(df, df2, on=None, left_on=None, right_on=None, how='inner', threshold=80):
    
    def fuzzy_apply(x, df, column, threshold=threshold):
        if type(x)!=str:
            return None
        
        match, score, *_ = process.extract(x, df[column], limit=1)[0]
            
        if score >= threshold:
            return match

        else:
            return None
    
    if on is not None:
        left_on = on
        right_on = on

    # create temp column as the best fuzzy match (or None!)
    df2['tmp'] = df2[right_on].apply(
        fuzzy_apply, 
        df=df, 
        column=left_on, 
        threshold=threshold
    )

    merged_df = df.merge(df2, how=how, left_on=left_on, right_on='tmp')
    
    del merged_df['tmp']
    
    return merged_df

Try it out using the example data:

df1 = pd.DataFrame({'Key':['Apple', 'Banana', 'Orange', 'Strawberry']})

df2 = pd.DataFrame({'Key':['Aple', 'Mango', 'Orag', 'Straw', 'Bannanna', 'Berry']})

fuzzy_merge(df, df2, on='Key', threshold=80)

Using thefuzz

Using SeatGeek’s great package thefuzz, which makes use of Levenshtein distance. This works with data held in columns. It adds matches as rows rather than columns, to preserve a tidy dataset, and allows additional columns to be easily pulled through to the output dataframe.

Sample data

df1 = pd.DataFrame({'col_a':['one','two','three','four','five'], 'col_b':[1, 2, 3, 4, 5]})

    col_a   col_b
0   one     1
1   two     2
2   three   3
3   four    4
4   five    5

df2 = pd.DataFrame({'col_a':['one','too','three','fours','five'], 'col_b':['a','b','c','d','e']})

    col_a   col_b
0   one     a
1   too     b
2   three   c
3   fours   d
4   five    e

Function used to do the matching

def fuzzy_match(
    df_left, df_right, column_left, column_right, threshold=90, limit=1
):
    # Create a series
    series_matches = df_left[column_left].apply(
        lambda x: process.extract(x, df_right[column_right], limit=limit)            # Creates a series with id from df_left and column name _column_left_, with _limit_ matches per item
    )

    # Convert matches to a tidy dataframe
    df_matches = series_matches.to_frame()
    df_matches = df_matches.explode(column_left)     # Convert list of matches to rows
    df_matches[
        ['match_string', 'match_score', 'df_right_id']
    ] = pd.DataFrame(df_matches[column_left].tolist(), index=df_matches.index)       # Convert match tuple to columns
    df_matches.drop(column_left, axis=1, inplace=True)      # Drop column of match tuples

    # Reset index, as in creating a tidy dataframe we've introduced multiple rows per id, so that no longer functions well as the index
    if df_matches.index.name:
        index_name = df_matches.index.name     # Stash index name
    else:
        index_name = 'index'        # Default used by pandas
    df_matches.reset_index(inplace=True)
    df_matches.rename(columns={index_name: 'df_left_id'}, inplace=True)       # The previous index has now become a column: rename for ease of reference

    # Drop matches below threshold
    df_matches.drop(
        df_matches.loc[df_matches['match_score'] < threshold].index,
        inplace=True
    )

    return df_matches

Use function and merge data

import pandas as pd
from thefuzz import process

df_matches = fuzzy_match(
    df1,
    df2,
    'col_a',
    'col_a',
    threshold=60,
    limit=1
)

df_output = df1.merge(
    df_matches,
    how='left',
    left_index=True,
    right_on='df_left_id'
).merge(
    df2,
    how='left',
    left_on='df_right_id',
    right_index=True,
    suffixes=['_df1', '_df2']
)

df_output.set_index('df_left_id', inplace=True)       # For some reason the first merge operation wrecks the dataframe's index. Recreated from the value we have in the matches lookup table

df_output = df_output[['col_a_df1', 'col_b_df1', 'col_b_df2']]      # Drop columns used in the matching
df_output.index.name = 'id'

id  col_a_df1   col_b_df1   col_b_df2
0   one         1           a
1   two         2           b
2   three       3           c
3   four        4           d
4   five        5           e

Tip: Fuzzy matching using thefuzz is much quicker if you optionally install the python-Levenshtein package too.

TheFuzz is the new version of a fuzzywuzzy

In order to fuzzy-join string-elements in two big tables you can do this:

1. Use apply to go row by row
2. Use swifter to parallel, speed up and visualize default apply function (with colored progress bar)
3. Use OrderedDict from collections to get rid of duplicates in the output of merge and keep the initial order
4. Increase limit in thefuzz.process.extract to see more options for merge (stored in a list of tuples with % of similarity)

‘*’ You can use thefuzz.process.extractOne instead of thefuzz.process.extract to return just one best-matched item (without specifying any limit). However, be aware that several results could have same % of similarity and you will get only one of them.

‘**’ Somehow the swifter takes a minute or two before starting the actual apply. If you need to process small tables you can skip this step and just use progress_apply instead

from thefuzz import process
from collections import OrderedDict
import swifter    


def match(x):
    matches = process.extract(x, df1, limit=6)
    matches = list(OrderedDict((x, True) for x in matches).keys())
    print(f'{x:20} : {matches}')

    return str(matches)


df1 = df['name'].values
df2['matches'] = df2['name'].swifter.apply(lambda x: match(x))

I’ve found this very efficient. The details in the description of the function:

from fuzzywuzzy.process import extract

def efficient_matching(df1,
                       col1,
                       df2,
                       col2,
                       limit=3,
                       length_diff=3,
                       first_letter_match=2
                       ):
    """
    For each name that we want to find matches for, it's more efficient to only look at a subset of potential matches. 
    One way to narrow down all the matches to potential matches is length. Here are 2 methods:
    1. If the name is "Markos", we don't need to check how similar markos is to names with length less than 4 or 
    more than 8. This window is determined by length_diff.
    2. We consider names from the corpus whose first 2 letters are similar the first letters of the name we want to find
    the match for.

    limit: Gives how many closest matches to return.
    """
    df1[col1] = df1[col1].astype(str)
    df2[col2] = df2[col2].astype(str)

    df1['_len_'] = df1[col1].apply(len)
    df2['_len_'] = df2[col2].apply(len)
    df2 = df2[df2['_len_'] >= 2]

    matches = df1[[col1, '_len_']].apply(lambda x: 
                                        extract(x[0], 
                                                df2[
                                                    ((df2['_len_'] - x[1]).abs() < length_diff) &
                                                    (df2[col2].str[:first_letter_match]==x[0][:first_letter_match])
                                                    ][col2].tolist(), 
                                                limit = limit
                                                ), 
                                        axis=1
                                        ) 
    return matches